




"""simple docstring"""




import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
					import torch






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"char"
				__UpperCamelCase         =		"bpe"
				__UpperCamelCase         =		"wp"




lowerCAmelCase				:     Dict					=				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		["image_processor", "char_tokenizer"]
				__UpperCamelCase         =		"ViTImageProcessor"
				__UpperCamelCase         =		"MgpstrTokenizer"





				def __init__(			self						,					_a=None						,					_a=None						,					**_a					):





							"""simple docstring"""
							lowerCamelCase      =   None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
										    """ instead."""						,					_a						,					)
										lowerCamelCase      =   kwargs.pop("""feature_extractor"""					)

							lowerCamelCase      =   image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("""You need to specify an `image_processor`."""					)
							if tokenizer is None:
										raise ValueError("""You need to specify a `tokenizer`."""					)

							lowerCamelCase      =   tokenizer
							lowerCamelCase      =   AutoTokenizer.from_pretrained("""gpt2"""					)
							lowerCamelCase      =   AutoTokenizer.from_pretrained("""bert-base-uncased"""					)

							super().__init__(_a						,					_a					)





				def __call__(			self						,					_a=None						,					_a=None						,					_a=None						,					**_a					):





							"""simple docstring"""
							if images is None and text is None:
										raise ValueError("""You need to specify either an `images` or `text` input to process."""					)

							if images is not None:
										lowerCamelCase      =   self.image_processor(_a						,					return_tensors=_a						,					**_a					)
							if text is not None:
										lowerCamelCase      =   self.char_tokenizer(_a						,					return_tensors=_a						,					**_a					)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										lowerCamelCase      =   encodings["""input_ids"""]
										return inputs





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   sequences
							lowerCamelCase      =   char_preds.size(0					)

							lowerCamelCase ,  lowerCamelCase      =   self._decode_helper(_a						,					"""char"""					)
							lowerCamelCase ,  lowerCamelCase      =   self._decode_helper(_a						,					"""bpe"""					)
							lowerCamelCase ,  lowerCamelCase      =   self._decode_helper(_a						,					"""wp"""					)

							lowerCamelCase      =   []
							lowerCamelCase      =   []
							for i in range(_a					):
										lowerCamelCase      =   [char_scores[i], bpe_scores[i], wp_scores[i]]
										lowerCamelCase      =   [char_strs[i], bpe_strs[i], wp_strs[i]]
										lowerCamelCase      =   scores.index(max(_a					)					)
										final_strs.append(strs[max_score_index]					)
										final_scores.append(scores[max_score_index]					)

							lowerCamelCase      =   {}
							lowerCamelCase      =   final_strs
							lowerCamelCase      =   final_scores
							lowerCamelCase      =   char_strs
							lowerCamelCase      =   bpe_strs
							lowerCamelCase      =   wp_strs
							return out





				def   _lowerCAmelCase							(			self						,					_a						,					_a					):





							"""simple docstring"""
							if format == DecodeType.CHARACTER:
										lowerCamelCase      =   self.char_decode
										lowerCamelCase      =   1
										lowerCamelCase      =   """[s]"""
							elif format == DecodeType.BPE:
										lowerCamelCase      =   self.bpe_decode
										lowerCamelCase      =   2
										lowerCamelCase      =   """#"""
							elif format == DecodeType.WORDPIECE:
										lowerCamelCase      =   self.wp_decode
										lowerCamelCase      =   102
										lowerCamelCase      =   """[SEP]"""
							else:
										raise ValueError(f'Format {format} is not supported.'					)

							lowerCamelCase ,  lowerCamelCase      =   [], []
							lowerCamelCase      =   pred_logits.size(0					)
							lowerCamelCase      =   pred_logits.size(1					)
							lowerCamelCase ,  lowerCamelCase      =   pred_logits.topk(1						,					dim=-1						,					largest=_a						,					sorted=_a					)
							lowerCamelCase      =   preds_index.view(-1						,					_a					)[:, 1:]
							lowerCamelCase      =   decoder(_a					)
							lowerCamelCase ,  lowerCamelCase      =   torch.nn.functional.softmax(_a						,					dim=2					).max(dim=2					)
							lowerCamelCase      =   preds_max_prob[:, 1:]

							for index in range(_a					):
										lowerCamelCase      =   preds_str[index].find(_a					)
										lowerCamelCase      =   preds_str[index][:pred_eos]
										lowerCamelCase      =   preds_index[index].cpu().tolist()
										lowerCamelCase      =   pred_index.index(_a					) if eos_token in pred_index else -1
										lowerCamelCase      =   preds_max_prob[index][: pred_eos_index + 1]
										lowerCamelCase      =   pred_max_prob.cumprod(dim=0					)[-1] if pred_max_prob.nelement() != 0 else 0.0
										dec_strs.append(_a					)
										conf_scores.append(_a					)

							return dec_strs, conf_scores





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   [seq.replace(""" """						,					""""""					) for seq in self.char_tokenizer.batch_decode(_a					)]
							return decode_strs





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return self.bpe_tokenizer.batch_decode(_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   [seq.replace(""" """						,					""""""					) for seq in self.wp_tokenizer.batch_decode(_a					)]
							return decode_strs









"""simple docstring"""




from collections.abc import Sequence



def        a__					(							snake_case__			,					snake_case__ = False							)		->   float:
			if not arr:
						return 0

			lowerCamelCase      =   0 if allow_empty_subarrays else float("""-inf"""							)
			lowerCamelCase      =   0.0
			for num in arr:
						lowerCamelCase      =   max(0 if allow_empty_subarrays else num			,					curr_sum + num							)
						lowerCamelCase      =   max(snake_case__			,					snake_case__							)

			return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					lowerCAmelCase				:     Union[str, Any]					=				[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")



